Estimating spinal age

ABSTRACT

An approach for a computer program to receive image data of a subject including at least a portion of a spine of the subject and a chronological age of the subject. The approach includes the computer program pre-processing the image data including at least a portion of a spine. The approach includes determining an apparent age of the spine or a portion of the spine of the subject using a trained artificial intelligence deep learning algorithm.

BACKGROUND

The present invention relates generally to the field of computer dataprocessing, and more particularly to a method to train a machinelearning algorithm as a functional spine age prediction model usingunsupervised deep learning.

Spinal degeneration manifests as changes to the spinal column that causechanges in the normal spine structure and/or spine function. Typically,back pain is the most common symptom of spinal degeneration. Back painfrom spinal degeneration ranges from mild or none in some patients todebilitating in other patients. Traditionally, one or more of severaltreatment options involving stretching and exercises for the patient areprescribed to reduce back pain or to prevent the worsening of spinaldegenerative changes in the patient. In some cases, patients experiencea reduction of back pain caused by spinal degeneration through the useof one or more of heat treatment, pain relief drugs, such asnonsteroidal anti-inflammatory drugs, acupuncture, or physiotherapy withspinal manipulation.

SUMMARY

Embodiments of the present provide a method, a computer program product,and a computer system for one or more computer processors to receiveimage data of a subject including at least a portion of a spine of thesubject and a chronological age of the subject. The method includes thecomputer processors pre-processing the image data including at least aportion of a spine and determining an apparent age of one of the spineor a portion of the spine of the subject using a trained artificialintelligence deep learning algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a functional block diagram of a computing environmentsuitable for operation of an apparent spine age program for a spine, aportion of the spine, a vertebra, or a spinal disc, in accordance withat least one embodiment of the invention.

FIG. 2 is an example of a schematic diagram of a method using a trainedapparent age program to determine an apparent spinal age of a spine in apatient, in accordance with at least one embodiment of the invention.

FIG. 3A is an illustration of a human spine with vertebrae labelling, inaccordance with at least one embodiment of the invention.

FIG. 3B is an illustration of a three-dimensional section of severalvertebra and disc, in accordance with at least one embodiment of theinvention.

3C is an illustration of a two-dimensional section of a vertebra, inaccordance with at least one embodiment of the invention.

FIG. 4 is an example of a program flow chart diagram depictingoperational steps for training the apparent spine age program, inaccordance with at least one embodiment of the invention.

FIG. 5 is a schematic diagram of a method of unsupervised deep learningfor training the apparent spine age program using an additional softmaxlayer, in accordance with at least one embodiment of the invention.

FIG. 6 is a schematic diagram of a method of unsupervised deep learningfor training the apparent age program using an autoencoder, inaccordance with at least one embodiment of the invention.

FIG. 7 is a schematic diagram of a method of unsupervised deep learningfor training the apparent spine age program using soft labels, inaccordance with at least one embodiment of the invention.

FIG. 8 is a block diagram depicting components of a computer systemsuitable for executing the apparent spine age program, in accordancewith at least one embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention recognize that back pain is themost common symptom of spinal degeneration. Back pain can range fromvery mild or none to debilitating for individuals with spinaldegeneration. Embodiments of the present invention recognize that theknown risk factors for spinal degeneration include a family history ofback pain or musculoskeletal disorders, excessive wear caused by heavylifting or sports, poor posture during prolonged sitting, excess weight,or an acute injury to the spine. Embodiments of the present inventionrecognize that a number of preventative measures can be taken to reduceor prevent back pain including exercises, stretching, drugs, heattreatment, or spinal manipulation using physiotherapy.

Embodiments of the present invention recognize that a direct correlationof back pain to spinal degeneration or a direct correlation of back painand spinal degeneration to risk factors associated with spinaldegeneration does not exist. Embodiments of the present inventionrecognize that some people with one or more of the known risk factorsfor spinal degeneration may have little to no spine degeneration andlittle to no back pain while other people with none of the known riskfactors for spine degeneration may have significant back pain and/orspine degeneration. Embodiments of the present invention recognize thatmethods of measuring osteoporosis of the vertebra or a disc of the spineexist. However, embodiments of the present invention recognize that amethod to evaluate multiple factors associated with spine structure todetermine spinal degeneration are not known.

Embodiments of the present invention recognize that a method to evaluatethousands of spine images to learn how to evaluate and apply an apparentspine age as a risk factor for spine degeneration would be advantageousfor patients. Embodiments of the present invention recognize it would bedesirable to identify individuals whose spine or a portion of theindividual's spine, such as a vertebra or a disc, exhibits a high degreeof spinal degeneration, beyond the amount of spine degeneration normallyobserved in individuals of a similar age. Embodiments of the presentinvention recognize that a program using trained machine learningalgorithms, such as deep artificial learning algorithms, that considersmultiple factors for identifying individuals whose functional orapparent spine age is significantly higher than their chronological agewould be beneficial. The factors for identifying individuals with afunctional spine age that is higher than the functional spine of otherindividuals of a similar chronological age are determined based on ananalysis of a very large number of spinal images during unsupervisedtraining of the machine learning algorithms. Embodiments of the presentinvention recognize an ability to detect individuals exhibiting greaterthan average spinal degeneration for their age group is advantageous inorder to apply known preventative measures prior to developing furtherspinal degeneration.

Embodiments of the present invention provide a method, a computerprogram, and a computer system for an objective assessment of a spine,as well as, an objective assessment of individual vertebra and discs inthe spine. Embodiments of the present invention provide a computerprogram using trained machine learning algorithms to determine anapparent age or functional age of an individual's spine as well as anapparent age of a portion of the spine, a single vertebra or a singledisc. The computer program using artificial intelligence algorithms withdeep learning networks trained using one of several unsupervisedtraining approaches on a plurality of spinal images where the pluralityof spinal images or scans ranges from hundreds to thousands of imagesfor training. The program with a trained deep learning network canoutput a functional or an apparent age of the spine or a portion of thespine based, at least in part, on the unsupervised training of the deeplearning network.

Embodiments of the present invention provide a computer program withmachine learning algorithms that are trained without manual annotationsor without the use of a ground truth. Embodiments of the presentinvention provide a computer program with machine learning algorithms,such as deep artificial intelligence algorithms, that are trained usingone of several unsupervised training approaches. Embodiments of thepresent invention provide a method of providing an output, such as, anapparent spine age that identifies individuals whose apparent spine ageis significantly higher than the individual's chronological age. In someembodiments, when the trained computer program identifies an apparentspine age of an individual that is greater than or significantly greatthan the individual's chronological age, a flag or a notation can beincluded in the analysis of the imaging or scan data even when the imageor the scan is not specifically of the spine or a portion of the spine.Embodiments of the present invention provide a computer program thatidentifies to medical professionals, with a notation or a flag, spinaldegradation in any computed tomography scan or magnetic resonanceimaging scan of an individual that includes the spine or a portion ofthe spine. In this way, the individual, once notified by medicalprofessionals of the spinal degeneration, can take preventive actions.

The present invention will now be described in detail with reference tothe Figures.

FIG. 1 is a functional block diagram of a computing environment 100suitable for operation of apparent spine age program 11 for a spine, aportion of the spine, a vertebra, or a spinal disc, in accordance withat least one embodiment of the invention. FIG. 1 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Implementation of embodiments of the invention may take avariety of forms, and exemplary implementation details are discussedsubsequently with reference to the Figures. Many modifications to thedepicted environment may be made by those skilled in the art withoutdeparting from the scope of the invention as recited by the claims.

Computing environment 100 includes computer 10, computed tomography (CT)scanner 15, and magnetic resonance imaging (MRI) scanner 5 connectedover network 110. Network 110 can be, for example, a local area network(LAN), a wide area network (WAN), such as the Internet, a virtual localarea network (VLAN), or any combination that can include wired,wireless, or optical connections. In general, network 110 can be anycombination of connections and protocols that will supportcommunications between computer 10, CT scanner 15, and MRI scanner 5 andany other computing devices not depicted in FIG. 1.

As depicted, computer 10 includes apparent spine age program 11, storage12, and user interface (UI) 13. In various embodiments, computer 10 canbe a desktop computer system, a mainframe computer, a laptop computer, aserver, a tablet computer, or any other programmable electroniccomputing device capable of receiving, sending, and processing data, andcommunicating with features and functions of CT scanner 15 and MRIscanner 5 and other computing devices not shown within computingenvironment 100 via network 110. In some embodiments, computer 10represents a computing system utilizing clustered computers andcomponents (e.g., database server computers, application servercomputers, etc.) that act as a single pool of seamless resources whenaccessed within distributed data processing environment 100, such as, acloud computing environment. Computer 10 may include internal andexternal hardware components, as depicted in more detail and describedin FIG. 8.

Apparent spine age program 11 is depicted as operating on computer 10.In various embodiments, once trained, apparent spine age program 11provides an evaluation of an apparent age of one of a person's spine ora portion of a person's spine based, at least in part, on received orretrieved data from one of CT scanner 15 or MRI scanner 5. In variousembodiments, apparent spine age program 11 receives or retrieves a largenumber of spinal images from one or more of CT scanner 15, MRI scanner5, or another imaging device for training to determine an apparentspinal age of a subject.

In various embodiments, once trained, apparent spine age program 11receives or retrieves one of CT scan data, CT volume data, MRI scandata, or X-ray data of a spine, a portion of a spine, a vertebra, or adisc to determine an apparent age of the spine, an apparent age of theportion of the spine, an apparent age of the vertebra, or an apparentage of the disc associated with the respective image data. In somecases, apparent spine age program 11 may receive CT scan data or MRIscan data captured for the evaluation of another organ or anotherportion of a patient's body, such as the digestive track, which alsoincludes the spine or a portion of the spine that may be used fortraining of the machine learning algorithm. In other cases, when atrained apparent spine age program 11 receives CT or MRI scans of otherorgans or body parts that include a patient's spine or a portion of thepatient's spine then, apparent spine age program 11 adds a notation of adetermined apparent spine age and/or a notation of one or more of anapparent spine age, an apparent vertebra age, or an apparent spinal discage that is significantly older than the patient's chronological age. Inthese embodiments, apparent spine age program 11 includes s pre-setnumber of years, such as, five years older, as a significant differencebetween the apparent spine age and the patient's chronological age. Whenthe pre-set number of years is exceeded, apparent spine age program 11adds a flag or a notation of significant spine, vertebra, or discdegeneration to the radiologist.

In various embodiments, apparent spine age program 11 pre-processesimage data, such as a CT scan, a CT volume, or an MRI scan. Thepre-processing can extract subvolumes containing the spine or a portionof the spine (e.g., three vertebra and two disc or a vertebra). Forexample, a CT volume can be pre-processed to extract subvolumes of aspine, of a portion of the spine, such as three vertebrae and two discs,a vertebra, or a disc. The extracted volume or subvolumes can be atwo-dimensional section or a three-dimensional section of the spine or aportion of the spine (e.g., a vertebra, a disc, etc.). Pre-processing ofreceived imaging data can be done for both the training of the deeplearning neural network and for an evaluation of a specific patient'simaging data by the trained apparent spine age program 11. In oneembodiment, apparent spine age program 11 resides on CT scanner 15. Inthis embodiment, once trained, apparent spine age program 11 retrievesCT scan data from storage (not depicted) in CT scanner 15 and determinesone of an apparent spinal age, an apparent vertebra age, or an apparentdisc age. In another embodiment, apparent spine age program 11 resideson MRI scanner 5. Apparent spine age program 11 may receive and senddata to and from CT scanner 15, MRI scanner 5, and other computerdevices not depicted within computing environment 100 using network 110.

In various embodiments, apparent spine age program 11 training occursusing a very large number of received or retrieved CT scans, CT volumes,MRI volumes, or X-rays associated with human spines or a portion ofhuman spines. FIGS. 5-7 depict a number of approaches to training one ormore deep learning networks in apparent spine age program 11 usingunsupervised training. In some embodiments, the deep learning algorithmsor deep learning networks in apparent spine age program 11 are one of aconvolutional neural network or a recurrent neural network. However, thedeep learning networks in apparent spine age program 11 are not limitedto one of a convolutional neural network or a recurrent neural network.

Apparent spine age program 11 may send an output, such as, an apparentspine age, an apparent disc age, an apparent age of a portion of thespine, or an apparent vertebra age, determined by the trained apparentspine age program 11 from an evaluation of a received CT scan or MRIscan to storage 12 in computer 10. In various embodiments, apparentspine age program 11, when trained, displays the apparent age of one ofthe patient's spine, a vertebra, or a disc captured in the CT scan dataor MRI scan data in UI 13. In some cases, apparent spine age program 11receives a user input on UI 13 to send an output or an apparent spineage to a computing device of a patient or a computing device of amedical professional. In some embodiments, apparent spine age program 11provides a notation in the output of apparent spine age program 11 oradds meta data associated with the received spine image data indicatingto a medical professional when significant spinal degeneration isdetermined.

CT scanner 15 is a computing device capable of performing a CT scan(also known as a computed axial tomography or CAT scan). CT scanner 15uses known medical imaging techniques that utilize computer-processedcombinations of multiple X-ray measurements taken from different anglesto produce tomographic (cross-sectional) images or virtual slices of abody. As depicted, CT scanner 15 includes storage 16 and UI 17. In oneembodiment, CT scanner 15 includes the computing processors andcomputing devices capable of executing apparent spine age program 11. Invarious embodiments, CT scanner 15 captures images of a patient's spinewith one or more vertebra and disc. In various embodiments, CT scanner15 receives a user input on UI 17 to send one or more spinal images tocomputer 10. CT scanner 15 may store captured spinal images in storage16. While depicted as a single CT scanner 15, more than one CT scanner15 can provide spinal image data to apparent spine age program 11 incomputer 10 and other computing devices not depicted in computerenvironment 100 over network 110.

MRI scanner 5 uses strong magnetic fields, magnetic field gradients, andradio waves to generate images of the organs in the body using knowntechnology. In various embodiments, MRI scanner 5 generates MRI imagesof a patient's spine. As depicted, MRI scanner 5 includes storage 6 andUI 7. In one embodiment, MRI scanner 5 includes the computing processorsand computing devices capable of executing apparent spine age program11. In an embodiment, MRI scanner 5 captures MRI images of the patient'sspine, sections of the patient's spine, a vertebra, or a disc. Invarious embodiments, MRI scanner 5 receives a user input on UI 7 to sendone or more spinal images to computer 10. MRI scanner 5 may storecaptured spinal images in storage 6. While depicted as a single MRIscanner 5, more than one MRI scanner 5 can provide spinal image data toapparent spine age program 11 in computer 10 and other computing devicesnot depicted in computer environment 100 over network 110.

FIG. 2 is an example of a schematic diagram of a method to determine anapparent spinal age of a patient using apparent spine age program 11, inaccordance with at least one embodiment of the invention. As depicted,FIG. 2 includes image data 20, chronological age data 22, pre-processing23, artificial intelligence (AI) deep learning 24, and apparent spineage 25, where a trained apparent spine age program 11 processes imagedata 20 and receives chronological age data 22 of the patient which isprocessed using AI deep learning 24 to output an apparent spine age ofthe patient's spine.

As depicted in FIG. 2, apparent spine age program 11, which has beentrained using many spinal images using one or more of the approachesdiscussed in detail later with respect to FIGS. 5-7, receives image data20. In various embodiments, image data 20 is from an image from apatient. The image data 20 can be one of the following: an image of acomplete spine, a portion of a spine, a vertebra, or a disc. Image data20 can be input into a trained apparent age program 11 from one of CTscanner 15 or MRI scanner 5 (depicted in FIG. 1), or other imagingdevice (e.g., an X-ray machine, etc.). In some embodiments, image data20 is one of a CT volume or an MRI scan. In some cases, apparent ageprogram 11 retrieves the image data 20 from one of storage 13, storage16, storage 6 or another storage location not depicted in FIG. 1. Asknown to one skilled in the art, volume rendering is a type of datavisualization technique which creates a three-dimensional representationof data. CT scan data and MRI scan data of the spine can be visualizedwith volume rendering to provide three-dimensional images of the spineor sections of the spine. In other embodiments, image data 20 is one ofa two-dimensional CT image, an MRI image, an X-ray, or other image ofthe patient's spine.

Upon receiving spinal image data 20, apparent spine age program 11pre-processes image data 20 in pre-processing 23. In various,embodiments, during pre-processing 23, apparent spine age program 11extracts one or more subvolumes from the CT volume. For example, duringpre-processing 23, the trained apparent spine age program 11 may extractone or more subvolumes from the CT volume containing the spine, aportion of the spine, a single vertebra or a single disc. The CT volumesor subvolumes can be a three-dimensional cross-section of the spine or aportion of the spine, such as, three vertebra and two discs, a vertebraor a disc. In some embodiments, two-dimensional sections of the spine, avertebra, or a disc are extracted by apparent spine age program 11.

In various embodiments, the extraction of CT subvolumes containingportions of the spine by apparent spine age program 11 is done bydetermining a center of a vertebra, by determining the center of eachvertebral bodies, or by determining a another landmark in each of thevertebra, such as, the center of the spinal cord, and then, extracting avolume of the CT scan. For example, apparent spine age program 11extracts the portion of the CT image that contains exactly one vertebraor a given vertebrae with a pre-determined number of adjacent vertebrae.In some cases, apparent spine age program 11 uses pre-processing 23 toextract a CT subvolume associated with each vertebra. In this case, thenumber of CT subvolumes would equal the number of vertebrae in a humanspine. In this example, apparent spine age program 11 could executesequentially on each vertebra in the patient's spine to produce anapparent spine age associated with the complete spine of the patient. Asimilar approach could be applied by apparent spine age program 11 forpre-processing 23 when image data 20 is MRI scan data.

In some embodiments, pre-processing 23 by apparent spine age program 11includes vertebrae labelling (e.g., C1, C2, and so on). In variousembodiments, when pre-processing 23 of image data 20 is complete, thenthe CT or MRI image data consists of a series of two-dimensionalsections or three-dimensional sections (e.g., subvolumes) of the entirespine or of a portion of the spine (e.g., vertebrae C1-C7). In somecases, when spinal surgery or spine augmentation has occurred (e.g.,kyphoplasty or vertebroplasty) then, the sections of the spine affectedby the surgery and/or therapeutic hardware are excluded from spinal ageanalysis by apparent spine age program 11. In some embodiments, thepre-processing of image data 20 in block 23, the data frompre-processing of image data 20 that occurs in block 23 is analyzed bythe trained AI deep learning algorithm 24 in apparent spine age program11 as a single volume (e.g., a vertebra or a complete spine). In otherembodiments, the pre-processed data is analyzed using AI deep learningalgorithm 24 as a sequence of CT sub-volumes or as a series oftwo-dimensional slices or sections of the patient's spine.

After completion of pre-processing of image data 20 in block 23,apparent spine age program 11 executes a trained machine learningalgorithm that is depicted as AI deep learning algorithm 24 usingchronological age data 22 of the patient that is input into apparentspine age program 11 by a user. A detailed discussion of some of theapproaches to unsupervised deep learning for possible methods to modelor estimate an apparent spine age without a ground truth (e.g., withoutknowing the apparent spinal age) are discussed in detail with regard toFIGS. 5-7. Upon receiving chronological age data 22, the trained AI deeplearning algorithm 24, executes as an apparent age prediction modelwithin apparent spine age program 11. Using AI deep learning algorithm,apparent spine age program 11 outputs apparent age 25 of the patient'sspine or apparent age 25 of a disc or vertebra of the patient.

As previously discussed, apparent age 25 of the patient is a functionalor apparent age of the spine or a portion of the patient's spinedetermined from image data 20 based, at least in part, on the trainingof AI deep learning algorithm 24 using vast amounts of image data ofspines. In various embodiments, apparent age 25 reflects the amount ofspinal degeneration commonly associated with a specific chronologicalage as determined from hundreds or thousands of spine imaging dataduring training of apparent spine age program 11. For example, for apatient, chronological age data 22 indicates that received chronologicalage of the patient is 51 years however, the analysis by apparent spineage program 11 using at least image data 20 and the trained AI deeplearning algorithm 24 indicates that the structure of the patient'sspine from image data 20 would typically be associated with a 65 yearold person.

In various embodiments, when apparent age 25 is greater by apre-determined amount pre-set in apparent spine age program 11 then,apparent spine age program 11 will add a notation or a comment to theoutput and/or as a notation or metadata associated with image data 20for the radiologist or another medical professional. For example, whenapparent age 25 output by apparent spine age program 11 is more thanfive years greater than the patient's chronological age then, apparentspine age program 11 adds a notation to the output and to image data 20of excessive spinal degeneration. In this case, a medical professional,upon viewing the output of apparent spine age program 11 with thenotation of excessive spine degeneration, may consider initiatingpreventive measures with the patient, such as, exercises or stretchesdesigned to slow or reverse the spinal degeneration.

FIG. 3A is an example of illustration 300A of human spine 30 withvertebrae labelling C1-L5 as captured in a scan of a patient, inaccordance with at least one embodiment of the invention. As depicted,FIG. 3A includes spine 30 composed of vertebrae C1-C7, T1-T12, L1-L5,and fused sacrum and coccyx 29 as may be captured by one of CT scanner15 or MRI scanner 5 depicted in FIG. 1. As known to one skilled in theart, the vertebrae of the spine are numbered and divided into regions,such as the cervical region that is composed of C1-C7 vertebrae, thethoracic region composed of T1-T12 vertebrae, the lumbar region composedof L1-L5 vertebrae, and sacrum and coccyx 29 that are fused together(from top to bottom of spine 30). The image of spine 30 can be retrievedfrom CT scanner 15, MRI scanner 5, storage 13, storage 16, storage 6, oranother storage location or database not depicted in FIG. 1.

In various embodiments, a CT or MRI scan of a patient provides acomplete or full image of spine 30. In other embodiments, a CT or MRIscan of a patient provides an image of a portion of spine 30. In sameembodiments, during pre-processing of the spine image data, apparentspine age program 11 determines a centerline through spine 30 (notdepicted in FIG. 3A) into to analyze spine image data and/or determinesubvolumes of the CT scan or MRI scan. As previously discussed, apparentspine age program 11 in computer 10 of FIG. 1 may determine an apparentspine age of spine 30 or a portion of spine 30 (e.g., one or more ofvertebra C1-L6 or any of the discs between adjacent vertebra ofvertebrae C1-L6) based, at least in part, on unsupervised training of amachine learning algorithm in apparent spine age program 11 using atraining set of retrieved spine images (e.g., greater than a thousandimages).

FIG. 3B is illustration 300B of a three-dimensional section of vertebra33, 35, and 37 with discs 34 a and 34 b, in accordance with at least oneembodiment of the invention. Illustration 300B is one example of aportion of a CT scan or a portion of CT volume of a portion of a spinethat is composed of vertebra 33, 35, and 37 with disc 34 a and 34 bbetween vertebra 33 and 35 and vertebra 35 and vertebra 37 respectively.For example, in some cases, the section of spine 30 depicted inillustration 300B may be a subvolume or a partial volume of a CT scanvolume captured by CT scanner 15. In other examples, illustration 300Bmay be an MRI scan or portion of an MRI scan of spine 30 in FIG. 1 thatis used by apparent spine age program 11 to determine an apparent age ofthe spine, an apparent age of a portion of the spine or for training ofapparent spine age program 11. Vertebra 33, 35, and 37 may be anyvertebra depicted in spine 30 in FIG. 1. In some embodiments, only oneof vertebra 33, 35, or 37 is extracted by apparent spine age program 11.While depicted as a three-dimensional section of spine 30 in FIG. 3A, inother embodiments, vertebra 33, 34, 37, and disc 34 a and 34 b could betwo-dimensional sections of spine 30. In other examples, vertebra 35with disc 34 a and 34 b may be extracted and used by apparent spine ageprogram 11. In some embodiments, apparent spine age program 11 extractsdisc 34 a for training of apparent spine age program 11 or fordetermining disc 34 a apparent age after training of apparent spine ageprogram 11.

FIG. 3C is illustration 300C of a two-dimensional section of vertebra38, in accordance with at least one embodiment of the invention. Asdepicted, FIG. 3C includes vertebra 38, spinal cord 39, and center 39 a.In some cases, center 39 a can be a center of vertebra 38, a center ofspinal cord 39, or another landmark for apparent spine age program 11 touse in pre-processing of the retrieved image data or scan data. In otherembodiments, a two-dimensional section of a CT scan, an MRI scan, or anX-ray of a disc, such as, disc 34 a in FIG. 3B, may be extracted or usedby apparent spine age program 11 to determine an apparent spine age.

FIG. 4 is a flow chart diagram depicting operational steps 400 fortraining apparent spine age program 11, in accordance with at least oneembodiment of the invention. As depicted, FIG. 4 includes apparent spineage program 11 receiving spine imaging data, apparent spine age program11 pre-processing spine imaging data, and training machine learningalgorithms in apparent spine age program 11 with unsupervised deeplearning using one or more of an additional softmax layer, anautoencoder, and soft labelling in apparent spine age program 11. FIG. 4depicts an example of training apparent spine age program 11 with vastamounts of spine imaging data and using at least one of three methods ofunsupervised deep learning (e.g., using an additional softmax layer,using an autoencoder, or using soft labelling).

In step 402, apparent spine age program 11 receives spine imaging data.In some embodiments, apparent spine age program 11 receives a user inputto retrieve spine imaging data from one or more of storage 13, storage17, storage 7, or another storage location or database not depicted inFIG. 1. As previously discussed with reference to FIG. 2, the receivedor retrieved spine imaging data can be one or more of a CT scan, a CTvolume, an MRI scan, an X-ray, a positron emission tomography (PET) scanusing a dye containing radioactive tracers, or other suitable images ofthe spine or a portion of the spine from more than one subject orhundreds or more different subjects (i.e., from many differentsubjects). In some cases, the spine imaging data may be incidentalspinal images captured during an evaluation of other organs or thespinal imaging data can spinal images captured for spine degenerationanalysis. In various embodiments, apparent spine age program 11 receivesor retrieves a large number of spine images captured by one of CTscanner 15 or MRI scanner 5 depicted in FIG. 1. For example, severalthousand spine images or several hundred spine images from differentsubjects can be received or retrieved for training of the deep learningAI algorithm in apparent spine age program 11.

In step 404, apparent spine age program 11 pre-processes spine imagingdata. The pre-processing of spine imaging data occurs as previouslydiscussed with regard FIG. 2. For example, for a given received orretrieved CT volume, apparent spine age program 11 may extract asubvolume of the received CT volume. The subvolume of the CT volume maycontain the full spine, a portion of the spine (e.g., 3 vertebrae andassociated disc), a specific vertebra, or a specific disc. Similarly,apparent spine age program 11 may extract a two-dimensionalcross-section of a vertebra (depicted in FIG. 3C), a disc, a portion ofthe spine, or the spine or a three-dimensional section of the spine or aportion of the spine (e.g., depicted in FIGS. 3A and 3B). In someembodiments, apparent spine age program 11 extracts a portion of aspine, a vertebra, a disc, or a complete spine from an MRI scansreceived by apparent spine age program 11. The extraction of the spineor a vertebra from a CT image, CT volume, an MRI scan, or an X-ray mayutilize a method of determining one of center of a specific spinal body,such as a center of a vertebra, a spinal cord (depicted in FIG. 3C), ora disc. In other cases, the distance from a landmark or a specified areaaround a specified landmark may be used by apparent spine age program 11to extract the volume or cross-section associated with a specified spineelement or a complete spine. In another method, apparent spine ageprogram 11 determines a centerline of the spine and determines a volumeadjacent to the centerline. The volume adjacent to the centerline cancontain two-dimensional cross-sections or three-dimensionalcross-sections perpendicular to the centerline of one or more vertebraor discs. In some cases, pre-processing would include labelling eachvertebra in each spine image as depicted in FIG. 3A.

In step 406, apparent spine age program 11 trains the machine learningalgorithm using unsupervised deep learning. Ground truth (GT) labels areused in supervised learning approaches. However, creating a ground truthfor an apparent spine age is impractical (e.g., one does not know theapparent spine age in advance). For example, a level of backpain issubjective and does not correlate well with a level of spinedegeneration. Spine degeneration assessments from different radiologistsare subjective assessments and may vary from radiologist to radiologistand would require many assessments of a spine image to be robust. Forthese reasons, an unsupervised deep learning approach can be used intraining apparent spine age program 11 instead of a traditionalsupervised learning approach using one or more GT labels.

In various embodiments, in apparent spine age program 11, in order toovercome an inability to provide a good GT, a latent variable, ŷ,indicates apparent spine age and a chronological age, y, is an observedvariable that is a random variable derived from some unknowndistribution p(y|ŷ). Although the distribution p(y|ŷ) is unknown, anassumption of a known functional form can be made (e.g., Gaussiandistribution, a gamma distribution, etc.). In various embodiments, aGaussian distribution is assumed for the distribution p(y|ŷ). In someembodiments, apparent spine age program 11 also uses a gammadistribution to account for possible asymmetry in the p(y|ŷ)distribution. In some cases, the distribution p(y|ŷ) is dependent onsome latent variable, for example, sigma. For example, when a Gaussiandistribution is assumed then, sigma is an unknown standard deviation ofthis distribution. In general, sigma can be a function of apparent spineage ŷ.

In various embodiments, the unsupervised training of the machinelearning or AI algorithm in step 406 occurs using one or more theapproaches discussed with respect to step 408, step 410, or step 412. Ingeneral, different approaches can be combined or ensembled to providemore accurate results.

In step 408, in one approach, apparent spine age program 11 trains themachine learning algorithm using unsupervised deep learning with anadditional softmax layer. During training, when the model in apparentspine age program 11 is determined with a deep learning neural networkwith a densely connected softmax layer then, another softmax layer isadded on top of the last densely connected softmax layer. As known toone skilled in the art, a softmax layer or a softmax function, which mayalso be known as a normalized exponential function, is a generalizationof the logical function to multiple dimensions and may be used inmultinomial logistical regression. In many cases, the softmax functionor the softmax layer can be used as the last activation function of aneural network to normalize the output of the neural network to aprobability distribution over predicted output classes. Often, thesoftmax layer can be implemented through a neural network layer justbefore the output layer.

In various embodiments, apparent spine age program 11 uses the deeplearning neural network with the additional softmax layer to model p (y,ŷ). During training, the deep neural network or deep learning network inapparent spine age program 11 learns the values of the weights of thedeep learning network and the parameters of p(y|ŷ) distribution (e.g.,like sigma) can be learned from the data providing the chronological ageof patients as a target variable. FIG. 5 depicts a schematic of apparentspine age program 11 with the deep learning neural network using anadditional softmax layer on top of the last densely connected softmaxlayer. Note that although apparent age is a continuous variable,apparent age is made discreet and a classification approach is used.

In step 410, in another approach, apparent spine age program 11 trainsthe machine learning algorithm using unsupervised deep learning using anautoencoder. In some cases, the approach discussed above relative tostep 408 when the objective function is maximized for ŷ=y then, the deeplearning network in apparent spine age program 11 can overfit the p(y|ŷ)distribution making it difficult to effectively train the deep learningnetwork. To correct for possible overfitting, an autoencoder can be usedto provide consistency and an estimate of the latent variable, ŷ. Inthis case, consistency can be achieved when similar spine images outputsimilar values for ŷ (e.g., similar apparent spine ages). As known toone skilled in the art, an autoencoder is a type of artificial neuralnetwork used to learn efficient data coding in an unsupervised mannerwhere the aim of an autoencoder is to learn a representation or encodingfor a set of data, typically for dimensionality reduction, by trainingthe network to ignore signal “noise”. Autoencoder can be consideredunsupervised or self-supervised because they generate their own labelsfrom the training data. An example of a schematic diagram of apparentspine age program 11 training the machine learning algorithm using anautoencoder for the unsupervised deep learning is depicted in FIG. 6.

In step 412, in yet another approach, apparent spine age program 11trains the machine learning algorithm using unsupervised deep learningusing soft labelling. In this embodiment, soft labelling is used with asoftmax regression with minimum entropy conditions. Minimum entropyencourages estimations which are precise. A soft label is a label whichhas a probability attached to it. For example, a probability, such as0.1, 0.3, or 0.5, (e.g., instead of a 0 or a 1) can be determined for asoft label. In some cases, soft labels may provide a model or network inapparent spine age program 11 with a more accurate description of theactual information about each sample or spine image. FIG. 7 withequation (1) depicts schematic diagram 700 with one example of trainingthe machine learning algorithm using unsupervised deep learning usingsoft labels. The details of step 412 are discussed in more detail laterwith respect to FIG. 7. In this case, the deep learning neural networkdoes not use the explicit form of p(y|ŷ) distribution (e.g., where thep(y|ŷ) distribution is assumed to be Gaussian). In this case, the deeplearning network in apparent spine age program 11 extractsnon-parametric form of p(y|ŷ) directly from the training data.

Once the training of the machine learning algorithm in apparent spineage program 11 occurs using one of the approaches in steps 408, 410 or412 as discussed in more detail later with regard to FIGS. 5, 6, and 7respectively, then, is some cases, a validation of apparent spine ageprogram 11 can be performed using one or more of various methods.

In one method, the validation of the training of apparent spine ageprogram 11 can be done indirectly when no ground truth labels areavailable (e.g., as discussed in step 408). For example, a set ofunlabeled spine images from a high-risk population (e.g., people withprevious back injuries, people with a family history of musculoskeletaldisorders, people with excessive wear of the spine due to sports orheavy lifting in jobs, etc.) to test if apparent spine age program 11output of apparent spine ages reflect the high-risk of this population.In this case, a test data set of approximately one thousand cases orpatients with a known chronological age and from this data set, ahistogram is created that approximates the distribution p(y) associatedwith chronological ages. Next, using the trained apparent spine ageprogram 11, determine a histogram or distribution p(ŷ) of the apparentspine age for the test data set. For example, using the equationP_(est)(y)=∫P(ŷ)p(y|ŷ) a test of the hypothesis that P_(est)(y) equalsP(ŷ) can occur.

Another method of validating the training of apparent spine age program11 could be done using ground truth labels created by human experts,such as, doctors or radiologists. In this method, the human expertsannotate the test data set. The experts can provide an apparent agebased on their experience and use this data to validate the model (e.g.,apparent spine age program 11). For example, another test data set ofapproximately one hundred cases or spine images are each examined by anexperienced radiologists who estimates the apparent spine age based atleast in part, on viewing one or more of axial images, CT volumerenderings of the spine, and/or other spine images or data. A comparisonof the output of an apparent spine age from trained apparent spine ageprogram 11 and the experienced radiologist apparent age estimate ofspine images and data can be done to determine if the output of anapparent spine age is the same or approximately the same as theradiologist determined apparent spine age in order to validate thetraining of apparent spine age program 11.

FIG. 5 is schematic diagram 500 of one method of unsupervised deeplearning for training the apparent spine age program using additionalsoftmax layer 54 a-54 n in step 408 of FIG. 4, in accordance with atleast one embodiment of the invention. FIG. 5 provides schematic diagram500 depicting additional details of the method of training deep learningnetwork 50 using unsupervised training with reference to step 408 ofFIG. 4. As depicted, FIG. 5 includes images 49, deep learning network50, softmax spine age classes 52 a-52 n, and additional softmax layer 54a-54 n for softmax chronological age y in apparent spine age program 11.Schematic diagram 500 is of a feed-forward network with the additionalsoftmax layer 54 a-54 n for the latent variable, ŷ, in softmax spine ageclasses for ŷ. Images 49 includes image data, such as, spine image data,vertebra image data, or disc image data is received by deep learningnetwork 50 for training in apparent spine age program 11. For training,images 49 can be hundreds or thousands of images 49 from hundreds orthousands of different subjects. After determining softmax spine ageclasses ŷ, the additional softmax layer 54 a-54 n models thedistribution p(y|ŷ), the unknown distribution.

Another variation of this approach uses noisy labels. In thisembodiment, the latent variable is assumed along with the model inapparent spine age program 11, then supervised training with noisylabels is performed. Any one of the known methods to apply noisy labelscan be used in this example.

FIG. 6 is illustration of a schematic diagram 600 of a method ofunsupervised deep learning for training apparent spine age program 11using an autoencoder, in accordance with at least one embodiment of theinvention. FIG. 6 provides a schematic diagram depicting additionaldetails of the method of training deep learning network 60 as themachine learning algorithm using unsupervised training with reference tostep 410 of FIG. 4. As depicted, FIG. 6 includes images 49, deeplearning network 60, spine age classes 62 a-62 n, where spine ageclasses 62 a-62 b are apparent spine age classes, inverse deep learningnetwork 64, and chronological age 66 a-66 n in apparent spine ageprogram 11, where deep learning network 60 receives image data (e.g.,complete spine, a portion of a spine, a vertebra, or a disc) from CTscanner 15, MRI scanner 5, (depicted in FIG. 1) or another source andinverse deep learning neural network 60 outputs images.

In the method discussed above with respect to FIG. 5, when the objectivefunction in equation (1) is maximized for ŷ=y then, deep learningnetwork 60 may overfit the objective function making training of deeplearning network difficult. In various embodiments, to account foroverfitting, an autoencoder is used with the additional softmax layerdiscussed above. In this embodiment, the autoencoder providesconsistency in addition to providing an apparent age as an output. Forexample, consistency is achieved when similar images or CT scans providesimilar apparent ages or ŷ as an output. As known to one skilled in theart, an autoencoder can have an input layer, an output layer, and anumber of one or more hidden layer connecting them, where the outputlayer has the same number of nodes as the input layer. The purpose ofthe autoencoder is to reconstruct the inputs to minimize the differencebetween the input and the output instead of predicting target value. Asdepicted in FIG. 6, the autoencoder is an inverse deep learning network.In FIG. 6, the input layer consumes images 49 and the output image isproduced by inverse network 64.

FIG. 7 is illustration of a schematic diagram 700 of a method ofunsupervised deep learning for training apparent spine age program 11using soft labels, in accordance with at least one embodiment of theinvention FIG. 7 provides schematic diagram 700 depicting additionaldetails of the method of training deep learning network 70 usingunsupervised training with reference to step 412 of FIG. 4. As depicted,FIG. 7 includes images 49, deep learning network 70, softmax spine ageclasses 72 a-72 n, and equation (1), where equation (1) is the objectivefunction for the softmax regression with minimum entropy. Deep learningnetwork 70 can receive large amounts of image data (e.g., hundreds orthousands of images) of CT scans, CT volumes, or MRI scans from CTscanner 15, MRI scanner 5, or another source from which image data ofspines or portions of spines may be extracted by apparent spine ageprogram 11 for unsupervised training of deep learning network 70. Theimage data extracted may be two-dimensional or three-dimensionalportions of a spine or of a complete spine, using the methods discussedearlier with respect to FIG. 4. FIG. 7 is an example of a softmaxregression network for soft labelling. In other embodiments, deeplearning network 70 receives hundreds or thousands of CT scans, CTvolumes, or MRI scans from which two-dimension or three-dimensionalsections of a vertebra or a disc can be extracted for training of deeplearning network 70 in apparent spine age program 11.

In equation (1), L(q,y) is the loss function, β is a hyperparameter thathas value from 0-1, and q_(k) is a probability of class k as determinedby deep learning network 70 (e.g., the output of softmax layer which isthe last layer of deep learning network 70). In various embodiments, theoptimal value of β is determined during the training. This method oftraining of apparent spine age program 11 can be applied to images of acomplete spine, two-dimensional or three-dimensional sections of aportion of the spine, two-dimensional or three-dimensional sections of avertebra or a disc.

In an embodiment, apparent spine age program 11 using a setup with asingle softmax layer as depicted in FIG. 7, the objective function forthe softmax regression with minimum entropy conditions is illustrated inequation (1), where q_(k) is a probability of class k that is the outputof the softmax layer that is the last layer of deep learning network 70,β is a hyperparameter with a value between 0 and 1 (e.g., the optimalvalue of β is determined during training), and y k is either 0 or 1depending on the chronological age of the subject. In equation (1), thesummation for the function goes from 18 to 100 since the subject agesare assumed to be between 18 and 100 years old.

FIG. 8 is a block diagram depicting components of a computer system 800suitable for executing apparent age program 11, in accordance with atleast one embodiment of the invention. FIG. 8 depicts a computer system800, which is representative of computer 10, in accordance with anillustrative embodiment of the present invention. It should beappreciated that FIG. 8 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Thecomponents of computer system of FIG. 8 are suitable for executingapparent age program 11. Many modifications to the depicted environmentmay be made. The computer system of FIG. 8 includes processor(s) 801,cache 803, memory 802, persistent storage 805, communications unit 807,input/output (I/O) interface(s) 806, and communications unit 807.Communications unit 807 provides communications between cache 803,memory 802, persistent storage 805, communications unit 807, andinput/output (I/O) interface(s) 806. Communications unit 807 can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system. For example,communications unit 807 can be implemented with one or more buses or acrossbar switch.

Memory 802 and persistent storage 805 are computer readable storagemedia. In this embodiment, memory 802 includes random access memory(RAM). In general, memory 802 can include any suitable volatile ornon-volatile computer readable storage media. Cache 803 is a fast memorythat enhances the performance of processor(s) 801 by holding recentlyaccessed data, and data near recently accessed data, from memory 802. Invarious embodiments, memory 802 and persistent storage 805 may storedata including the results of apparent age program 11, CT scan data,and/or MRI scan data.

Program instructions and data (e.g., software and data 810) used topractice embodiments of the present invention may be stored inpersistent storage 805 and in memory 802 for execution by one or more ofthe respective processor(s) 801 via cache 803. In an embodiment,persistent storage 805 includes a magnetic hard disk drive.Alternatively, or in addition to a magnetic hard disk drive, persistentstorage 805 can include a solid state hard drive, a semiconductorstorage device, a read-only memory (ROM), an erasable programmableread-only memory (EPROM), a flash memory, or any other computer readablestorage media that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 805 may also be removable. Forexample, a removable hard drive may be used for persistent storage 805.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage805. Software and data 810 can be stored in persistent storage 805 foraccess and/or execution by one or more of the respective processor(s)801 via cache 803.

Communications unit 807, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 807 includes one or more network interface cards.Communications unit 807 may provide communications through the use ofeither or both physical and wireless communications links usingcommunication fabric 804. Program instructions and data (e.g., softwareand data 810) used to practice embodiments of the present invention maybe downloaded to persistent storage 805 through communications unit 807.

I/O interface(s) 806 allows for input and output of data with otherdevices that may be connected to each computer system. For example, I/Ointerface(s) 806 may provide a connection to external device(s) 808,such as a keyboard, a keypad, a touch screen, and/or some other suitableinput device. External device(s) 808 can also include portable computerreadable storage media, such as, for example, thumb drives, portableoptical or magnetic disks, and memory cards. Program instructions anddata (e.g., software and data 810) used to practice embodiments of thepresent invention can be stored on such portable computer readablestorage media and can be loaded onto persistent storage 805 via I/Ointerface(s) 806. I/O interface(s) 806 also connect to display 809.

Display 809 provides a mechanism to display data to a user and may be,for example, a computer monitor.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may execute aresource entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method, thecomputer-implemented method comprising: receiving, by one or morecomputer processors, image data of a subject including at least aportion of a spine of the subject and a chronological age of thesubject; pre-processing, by one or more computer processors, the imagedata including at least the portion of a spine; and determining, by oneor more computer processors, an apparent age of one of the spine or theportion of the spine of the subject using a trained artificialintelligence deep learning algorithm.
 2. The computer-implemented methodof claim 1, wherein receiving the image data of the subject including atleast a portion of the spine of the subject includes receiving one of acomputed tomography (CT) scan, a CT volume, or a magnetic resonanceimaging (MRI) scan of at least a portion of the spine of the subject. 3.The computer-implemented method of claim 2, wherein pre-processing theimage data including at least a portion of the spine of the subjectincludes extracting one of the spine or the portion of the spine fromone of the CT scan, the CT volume, or a MRI scan.
 4. Thecomputer-implemented method of claim 3, wherein the spine or the portionof the spine extracted from the image data is one of a two-dimensionalsection or a three-dimensional section of the spine or the portion ofthe spine, and wherein the portion of the spine includes one of avertebra, a disc, or a combination of more than one vertebra and one ormore disc of the spine of the subject.
 5. The computer-implementedmethod of claim 1, wherein determining the apparent age of one the spineor the portion of the spine of the subject using the trained artificialintelligence deep learning algorithm, comprises: retrieving, by one ormore computer processors, a plurality of spine image data of a pluralityof subjects for training the artificial intelligence deep learningalgorithm; pre-processing, by one or more computer processors, theplurality of the spine image data of the plurality of subjects;training, by one or more computer processors, a machine learningalgorithm using unsupervised deep learning wherein, the machine learningalgorithm is a deep learning network wherein, a latent variableindicates an apparent spine age, the chronological age of the subject isa random variable derived from a distribution of a plurality ofprobabilities of the chronological age with respect to the apparent ageand wherein the deep learning network learns the distribution of theplurality of probabilities chronological age with respect to theapparent age from the training using the plurality of image data.
 6. Acomputer-implemented method of claim 5, wherein training the machinelearning algorithm using unsupervised deep learning includes using adeep learning network with an additional softmax layer on top of a lastdensely connected softmax layer.
 7. A computer-implemented method ofclaim 6, wherein training the machine learning algorithm usingunsupervised deep learning comprises adding an inverse deep learningnetwork as an autoencoder.
 8. The computer-implemented method of claim5, wherein training the machine learning algorithm using unsuperviseddeep learning includes using soft labelling and minimum entropy with asoftmax regression.
 9. The computer-implemented method of claim 1,further comprising adding a notation of excessive spinal degenerationassociated with one of the spine or the portion of the spine to anoutput when then apparent age of one of the spine or a portion of thespine of the subject is greater than the chronological age of thesubject by more than a pre-set number of years.
 10. A computer programproduct for determining an apparent spine age, the computer programproduct comprising: one or more computer readable storage media; andprogram instructions stored on the one or more computer readable storagemedia, the program instructions executable by a processor, the programinstructions comprising instructions for: receiving image data of asubject including at least a portion of a spine of the subject and achronological age of the subject; pre-processing the image dataincluding at least the portion of a spine; and determining an apparentage of one of the spine or the portion of the spine of the subject usinga trained artificial intelligence deep learning algorithm.
 11. Thecomputer program product of claim 10, wherein receiving the image dataof the subject including at least a portion of the spine of the subjectincludes receiving one of a computed tomography (CT) scan, a CT volume,or a magnetic resonance imaging (MRI) scan of at least a portion of thespine of the subject.
 12. The computer program product of claim 11,wherein pre-processing the image data including at least a portion ofthe spine of the subject includes extracting one of the spine or theportion of the spine from one of the CT scan, the CT volume, or a MRIscan.
 13. The computer program product of claim 12, wherein the spine orthe portion of the spine extracted from the image data is one of atwo-dimensional section or a three-dimensional section of the spine orthe portion of the spine, and wherein the portion of the spine includesone of a vertebra, a disc, or a combination of more than one vertebraand one or more disc of a spine of the subject.
 14. The computer programproduct of claim 10, wherein determining the apparent age of one thespine or the portion of the spine of the subject using the trainedartificial intelligence deep learning algorithm, comprises: retrieving aplurality of spine image data of a plurality of subject for training theartificial intelligence deep learning algorithm; pre-processing theplurality of the spine image data of the plurality of subjects; traininga machine learning algorithm using unsupervised deep learning wherein,the machine learning algorithm is a deep learning network wherein, alatent variable indicates an apparent spine age, the chronological ageof the subject is a random variable derived from a distribution of aplurality of probabilities of the chronological age with respect to theapparent age and wherein the deep learning network learns thedistribution of the plurality of probabilities chronological age withrespect to the apparent age from the training using the plurality of thespine image data of the plurality of subjects.
 15. The computer programproduct of claim 14, wherein training the machine learning algorithmusing unsupervised deep learning includes using a deep learning networkwith an additional softmax layer on top of a last densely connectedsoftmax layer.
 16. The computer program product of claim 15, whereintraining the machine learning algorithm using unsupervised deep learningcomprises adding an inverse deep learning network as an autoencoder. 17.The computer program product of claim 14, wherein training the machinelearning algorithm using unsupervised deep learning includes using softlabelling and minimum entropy with a softmax regression.
 18. Thecomputer program product of claim 10, further comprising adding anotation of excessive spinal degeneration associated with one of thespine or the portion of the spine to an output when then apparent age ofone of the spine or a portion of the spine of the subject is greaterthan the chronological age of the subject by more than a pre-set numberof years.
 19. A computer system comprising: one or more computerprocessors; one or more computer readable storage media; programinstructions stored on the one or more computer readable storage mediafor execution by at least one of the one or more processors, the programinstructions comprising instructions to perform: receiving image data ofa subject including at least a portion of a spine of the subject and achronological age of the subject; pre-processing the image dataincluding at least the portion of a spine; and determining an apparentage of one of the spine or the portion of the spine of the subject usinga trained artificial intelligence deep learning algorithm.
 20. Thecomputer system of claim 19, wherein determining the apparent age of onethe spine or the portion of the spine of the subject using the trainedartificial intelligence deep learning algorithm, comprises: retrieving aplurality of spine image data of a subjects for training the artificialintelligence deep learning algorithm; pre-processing the plurality ofthe spine image data of the plurality of subjects; and training amachine learning algorithm using unsupervised deep learning wherein, themachine learning algorithm is a deep learning network wherein, a latentvariable indicates an apparent spine age, the chronological age of thesubject is a random variable derived from a distribution of a pluralityof probabilities of the chronological age with respect to the apparentage and wherein the deep learning network learns the distribution of theplurality of probabilities chronological age with respect to theapparent age from the training using the plurality of image data.